---
pageType: source
id: source.life-view-dashboard
title: life-view-dashboard
sourceType: local-file
sourcePath: /home/topher/.openclaw/workspace-crash-bot/projects/life-view-dashboard.md
ingestedAt: 2026-05-02T21:17:45.330Z
updatedAt: 2026-05-02T21:17:45.330Z
status: active
growth: seed
---

# life-view-dashboard

## Source
- Type: `local-file`
- Path: `/home/topher/.openclaw/workspace-crash-bot/projects/life-view-dashboard.md`
- Bytes: 7658
- Updated: 2026-05-02T21:17:45.330Z

## Content
```text
# Personal Knowledge Companion

## Concept
A persistent knowledge layer across all agents that maps what you know, what you're learning, and where your gaps are — then tells you about those gaps proactively.

The visual/exploration layer is a **knowledge graph** — relationships and connections, not just stored documents. This makes gap detection natural: missing edges in the graph *are* the gaps.

Domain separation:
- 🏫 **School** — 2890 bot (robotics team)
- 🏭 **Work** — PSB bots (brewery ops)
- 🎮 **Play** — crash-bot / HHS-Hackers crew

## Core Insight
Current agents are mostly command-and-reply. They don't do research, self-direct, or maintain persistent context between sessions. They feel like fancy autocomplete, not assistants.

The question: what would make them feel less like tools and more like... something with continuity?

## Architecture

### The Silo Problem
Three agent domains are intentionally isolated:
- 🏫 School (2890) — student data, competition strategy
- 🏭 Work (PSB) — brewery ops, business data
- 🎮 Play (HHS-Hackers) — crew knowledge, projects

The wiki needs to cross domains without breaking the walls. Solution: a librarian agent.

### Multi-Person Scaling
Same shared knowledge graph, different personal layers per person:
- Each person gets an entity page with confidence scores, scale preferences, learning style
- Shared facts (ESP32 specs, VLAN concepts) compound as every person's learning adds resources
- Access control maps to existing Discord server structure
- Kyle's CEH training → HHS-Hackers server
- Bruno's PathPlanner → 2890 server
- Your VLAN training → DMs or HHS-Hackers
- The compounding effect: every person's learning path makes the knowledge graph richer for the next person

### Professor Agent (The Teacher)
Not just a librarian — a **teacher** that:
1. Sees what you're working on (Pi-hole setup → VLANs coming next)
2. Assesses where you are ("almost none" on VLANs)
3. Finds resources matched to your scale (home lab, not enterprise)
4. Organizes into a learning path (training thread under VLAN)
5. Delivers proactively without being asked

The difference: A librarian says "here's everything filed under VLAN." A professor says "here's what you need right now, at your level, for your situation."

**How it works with the wiki stack:**
- Project entities have required skill tags (Pi-hole → DNS, networking, VLANs)
- Person entities have confidence scores per skill (VLANs: 0.1, never touched)
- Professor maps required skills against known skills → finds gaps
- Searches curated sources scoped to your context (home-lab scale, not enterprise)
- Delivers as a Discord training thread, which itself becomes wiki content
- When you come back and say "I get VLANs now", professor updates the claim
- Every person's learning path compounds into the shared knowledge graph

**Context-aware filtering** — the professor knows your scale:
- `bestUsedFor: home-lab, small-scale, hands-on learning`
- `notEnoughFor: enterprise, production, large-scale`
- This turns "47 VLAN resources" into "3 that match your setup"

### Vault Structure
Each agent owns its own vault partition. Professor reads metadata across partitions and writes to a shared cross-reference layer. Walls stay up.

### Layers
1. **memory-wiki** — Storage layer. Bridge mode. Obsidian-compatible. Structured claims with provenance and confidence.
2. **Knowledge graph** — The exploration/visual layer. Maps relationships and connections. Missing edges = gaps.
3. **Gap detection** — Runs on the professor. Proactive intelligence. Surfaces what's stale, what's missing, what's the natural next step.
4. **Professor agent** — The teacher. Cross-domain reader, context-aware resource finder, learning path organizer. Bridges the silos without breaking them.

Build order: wiki config → professor agent → gap detection → knowledge graph visualization

### Exec Power Constraint
- **crash-bot-DM (this agent, -topher's DMs):** HAS exec. Can build, wire, and touch the system.
- **crash-bot-public (server channels):** No exec. Config changes must be drafted here and applied by -topher or crash-bot-DM.
- **crash-bot-public exec** will be gated via `exec-approvals.json` allowlist + ask mode — commands require -topher's approval via `/approve`.

## Exec Approval Setup
See: `projects/exec-approval-setup.md`

**Status:** Partially implemented. Waiting for gateway reboot to complete configuration.
### What's done:
- [x] Backed up `openclaw.json` → `openclaw.json.pre-exec-approval-20260502-120941`
- [x] Backed up `exec-approvals.json` → `exec-approvals.json.pre-setup-20260502-120941`
- [x] Updated `exec-approvals.json` with full policy (crash-bot-public: allowlist+ask, crash-bot: pre-approved read commands)
### What's blocked:
- [ ] Update `openclaw.json` agent tool configs for exec settings
- [ ] Restart gateway and verify exec works
- [ ] Test approval flow

**Blocker:** Gateway rejecting all exec with "pairing required" — full system reboot needed

## What's Missing
The "no research" problem. Agents respond when called but don't:
- Proactively gather information
- Maintain long-term context outside of session
- Work on background tasks between interactions

## Memory File
See: `memory/personal-knowledge-companion.md` — full concept, references, and status

## Status
- [x] Concept documented
- [x] Discord channel (#personal-knowledge-companion)
- [x] Knowledge graph (not Empire visual) as exploration layer
- [x] Obsidian as client for vault browsing/graph
- [x] memory-wiki plugin research complete (bridge mode, Obsidian render)
- [x] Professor agent vision documented (teacher, not librarian; multi-person scaling)
- [x] Multi-person scaling architecture documented
- [x] Exec approval setup documented (projects/exec-approval-setup.md)
- [ ] exec enabled for crash-bot-public (awaiting crash-bot-DM implementation)
- [ ] memory-wiki plugin enabled and configured
- [ ] Vault structure created (entities, concepts, syntheses, sources, reports)
- [ ] Bridge mode connected to active memory
- [ ] Professor agent
- [ ] Gap detection logic
- [ ] Knowledge graph visualization (Obsidian graph view)

## memory-wiki Plugin Research
- Built into OpenClaw, no extra install needed
- Vault modes: isolated (own vault), bridge (reads active memory artifacts), unsafe-local (escape hatch)
- Recommended: **bridge mode** — QMD for recall, wiki for synthesized knowledge
- Vault layout: entities/, concepts/, syntheses/, sources/, reports/, _views/
- Obsidian-compatible render mode built in
- Structured claims with confidence, provenance, evidence
- Auto-generates dashboards: open-questions, contradictions, low-confidence, stale-pages, relationship-graph
- Tools: wiki_search, wiki_get, wiki_apply, wiki_lint, wiki_status
- Config path: plugins.entries.memory-wiki.config
- Vault default path: ~/.openclaw/wiki/main
- Bridge mode indexes: memory artifacts, dream reports, daily notes, memory root files, memory events

### Hardware Constraints
Running on Dell Aurora-R3 (i7-2600K, 32GB DDR3). File-based wiki = zero compute cost. Obsidian = client-side only, no server. Qdrant already running. Gap detection = lightweight heartbeat task. No roadblocks on the core build — only a real-time interactive graph server would strain this box, and Obsidian's local graph view sidesteps that entirely.

## Related
- Claw Empire (GitHub: GreenSheep01201/claw-empire) — separate project for later. Different problem space (orchestration/workflow), not the visual layer for this.
- Mission Control — already has agent topology, could be foundation
```

## Notes
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## Related
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- No related pages yet.
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